الملخص الإنجليزي
Many methods were developed to get a correct velocity estimate of the subsurface.
Erroneous velocities can cause incorrect positioning of reflectors, which thus affects
the imaging quality. Technology has been developed to have better velocity estimation.
From diving wave, and ray path tomography to full wave inversion. However, most
modeling techniques have shown limitations in constructing an accurate velocity for
the near-surface. Surface waves are used today in a wide range of near-surface
modeling. It has shown effectiveness in overcoming hidden layers and velocity
inversions within the near-surface. A large portion of the area within central Oman is
covered by dunes. The quality of the surface wave picking depends on seismic data
quality. The dune noise weakens the ground-roll energy. Furthermore, the fundamental
mode within dune areas is biased since several modes were mixed, which affects the
surface wave modeling process. Unsupervised machine learning was implemented to
segregate the dunes area from the non-dune to apply different preconditioning for
both areas. Then the areas were combined to build a guide function representing the
dispersion profile at each location to drive the surface-wave picking. The inverted
velocity using surface wave tomography shows a better correlation with topography
when machine learning is applied. As it was able to capture the velocity variations
within the area with a high resolution.
